Feature Selection Intent Machine Learning based Conjecturing Workout Burnt Calories

Abstract

As we know that running is the victor for most calories burned per hour. Stationary bicycling, running, and swimming are fabulous choices as well. HIIT works out are too incredible for burning calories. After a HIIT workout, your body will proceed to burn calories for up to 24 hours. Forecasting the workout burnt calories still remains an open challenge as the changes in the environmental calamity and body health. The machine learning strategies can predict the burnt out calories for the course of exercise done by a body. With this background, we have utilized Exercise dataset extracted from UCI Machine Learning repository for predicting the workout burnt calories.  The forecasting of burnt calories rate are achieved in four ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory feature examination is done and the scattering of target highlight is visualized. Thirdly, the raw data set is fitted to all the regressors and the execution is dissected before and after scaling. Fourth, the raw data set is subjected to feature selection axioms like Anova test, Correlated Feature, Variance Based and KBest Feature based methods and are fitted to all the regressors and the performance is analyzed before and after feature scaling. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Decision Tree and Gradient Boosting regressor tends to retain 99% before and after feature scaling for the Anova test, Correlated Feature, Variance Based and KBest Feature based methods.